Morphological and Physical Diversity of Mangoes (Mangifera indica L.) of Local Varieties Found in Noun and Lekié Localities (Cameroon)
Bibliographic record
Abstract
Cameroon has an amazing variety of local mangoes whose potential is poorly exploited. The aim of this study was to characterise the physical and morphological diversity of mangoes in two agro-ecological regions with high potential for mango production. This experiment was conducted between February and July 2021 using ten local mango varieties. These were 'German', 'Bamoun', 'Lady' and 'American' mangoes found in Noun and 'Papaya', 'Dshang Dshang 1', 'Dshang Dshang 2', 'Kousa Dog', 'Garoua' and 'Ladies' mangoes identified in Lekié. Ten ripe fruits of each variety were harvested on three different trees in the same area. A total of 23 morphological and physical parameters were measured. Multivariate analysis based on PCA showed four groups of varieties in decreasing order of importance: group 2 (Papaya, German and American mangoes), group 4 (Garoua, Dame Lékie, Kousa Dog), group 3 (Dshang Dshang 2, Dame Noun, Dshang Dshang 1) and group 1 (Bamoun). Group 2 varieties had good quality for pulp mass to stone mass ratio (5.58±1), size index (10.6±3.22), sphericity index (0.97±0.35), fruit volume (391.5) and lateral fruit diameter (11.05±0.89). However, varieties in group 1 (12.87±3.08) and group 3 (10.7±2.27) have a high proportion of kernels in the fruit and a high kernel density, respectively. There is a wide diversity among the varieties examined. This provides valuable information of the different stakeholders in the mango value chain, i.e., the industry, nurserymen and consumers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".